An adaptive scheme for quantum state tomography

Crescimanna, Valerio (2019) An adaptive scheme for quantum state tomography. [Laurea magistrale], Università di Bologna, Corso di Studio in Fisica [LM-DM270]
Documenti full-text disponibili:
[img] Documento PDF (Thesis)
Disponibile con Licenza: Creative Commons: Attribuzione - Non commerciale - Condividi allo stesso modo 3.0 (CC BY-NC-SA 3.0)

Download (6MB)

Abstract

The process of inferring and reconstructing the state of a quantum system from the results of measurements, better known as quantum state tomography, constitutes a crucial task in the emerging field of quantum technologies. Today it is possible to experimentally control quantum systems containing tens of entangled qubits and perform measurements of arbitrary observables with great accuracy. However, in order to complete characterize an unknown $n$-qubit state, quantum state tomography requires a number of measurements which grows exponentially with $n$. A possible way to avoid this problem consists in performing an incomplete tomographic procedure able to provide a good estimate of the true state with few measurements. This thesis proposes a scheme for $n$-qubit state tomography which aims to improve the fidelity between the reconstructed state and the target state. In particular, the scheme identifies the next measurement to perform based on the knowledge already acquired from the previous measurements on the experimental prepared state. The performance of this scheme was finally analyzed by means of simulations of quantum state tomography with product measurements as well as with entangled measurements. In both cases one observes that the here proposed adaptive scheme significantly outperforms a standard scheme in terms of the fidelity of the reconstructed state.

Abstract
Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Crescimanna, Valerio
Relatore della tesi
Correlatore della tesi
Scuola
Corso di studio
Indirizzo
Curriculum A: Teorico generale
Ordinamento Cds
DM270
Parole chiave
Adaptive,Quantum tomography
Data di discussione della Tesi
13 Dicembre 2019
URI

Altri metadati

Statistica sui download

Gestione del documento: Visualizza il documento

^